Object/Class

org.apache.flink.ml.nn

KNN

Related Docs: class KNN | package nn

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object KNN

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  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. object Blocks extends Parameter[Int] with Product with Serializable

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  5. object DistanceMetric extends Parameter[DistanceMetric] with Product with Serializable

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  6. object K extends Parameter[Int] with Product with Serializable

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  7. object SizeHint extends Parameter[CrossHint] with Product with Serializable

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  8. object UseQuadTree extends Parameter[Boolean] with Product with Serializable

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  9. def apply(): KNN

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  10. final def asInstanceOf[T0]: T0

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  11. def clone(): AnyRef

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  12. final def eq(arg0: AnyRef): Boolean

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  13. def equals(arg0: Any): Boolean

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  14. def finalize(): Unit

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  15. implicit def fitKNN[T <: Vector](implicit arg0: TypeInformation[T]): FitOperation[KNN, T]

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    FitOperation which trains a KNN based on the given training data set.

    FitOperation which trains a KNN based on the given training data set.

    T

    Subtype of org.apache.flink.ml.math.Vector

  16. final def getClass(): Class[_]

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  17. def hashCode(): Int

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  18. final def isInstanceOf[T0]: Boolean

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  19. final def ne(arg0: AnyRef): Boolean

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  20. final def notify(): Unit

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  21. final def notifyAll(): Unit

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  22. implicit def predictValues[T <: Vector](implicit arg0: ClassTag[T], arg1: TypeInformation[T]): PredictDataSetOperation[KNN, T, (Vector, Array[Vector])]

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    PredictDataSetOperation which calculates k-nearest neighbors of the given testing data set.

    PredictDataSetOperation which calculates k-nearest neighbors of the given testing data set.

    T

    Subtype of Vector

    returns

    The given testing data set with k-nearest neighbors

  23. final def synchronized[T0](arg0: ⇒ T0): T0

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  24. def toString(): String

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  25. final def wait(): Unit

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  26. final def wait(arg0: Long, arg1: Int): Unit

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  27. final def wait(arg0: Long): Unit

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